Orringer, DanielPandian, BalajiNiknafs, YasharHollon, ToddBoyle, JulianneLewis, SpencerGarrard, MiaHervey-Jumper, ShawnGarton, HughMaher, CormacHeth, JasonSagher, OrenWilkinson, D.Snuderl, MatijaVenneti, SriramRamkissoon, Shakti H.McFadden, KathrynFisher-Hubbard, AmandaLieberman, AndrewJohnson, TimothyXie, XiaoliangTrautman, JayFreudiger, ChristianCamelo-Piragua, Sandra2017-07-202017Orringer, Daniel A., Balaji Pandian, Yashar S. Niknafs, Todd C. Hollon, Julianne Boyle, Spencer Lewis, Mia Garrard, et al. 2017. “Rapid Intraoperative Histology of Unprocessed Surgical Specimens via Fibre-Laser-Based Stimulated Raman Scattering Microscopy.” Nature Biomedical Engineering 1 (2) (February 6): 0027. doi:10.1038/s41551-016-0027.2157-846Xhttp://nrs.harvard.edu/urn-3:HUL.InstRepos:33471123Conventional methods for intraoperative histopathologic diagnosis are labor- and time-intensive and may delay decision-making during brain tumor surgery. Stimulated Raman scattering (SRS) microscopy, a label-free optical process, has been shown to rapidly detect brain tumor infiltration in fresh, unprocessed human tissues. Previously, the execution of SRS microscopy in a clinical setting has not been possible. We report the first demonstration of SRS microscopy in an operating room using a portable fiber-laser-based microscope in unprocessed specimens from 101 neurosurgical patients. Additionally, we introduce an image-processing method, stimulated Raman histology (SRH), which leverages SRS images to create virtual hematoxylin and eosin- stained slides, revealing essential diagnostic features. In a simulation of intraoperative pathologic consultation in 30 patients, the concordance of SRH and conventional histology for predicting diagnosis was nearly perfect (κ>0.89) and accuracy exceeded 92%. We also built and validated a multilayer perceptron based on quantified SRH image attributes that predicts brain tumor subtype with 90% accuracy. This study provides insight into how SRH can now be used to improve the surgical care of brain tumor patients.en-USBiomedical engineeringCNS cancerImage processingLasers, LEDs and light sourcesSurgical oncologyJournal Article2016-12-122017-08-0710.1038/s41551-016-0027